Researchers have introduced a new perspective on uncertainty quantification (UQ) by proposing that uncertainty measures are not fundamental but rather derived from higher-level modeling decisions. This framework shows how epistemic and aleatoric uncertainties can be obtained through the decomposition of subjective risk, using strictly proper loss functions. The approach unifies various UQ measures under a common theoretical foundation and suggests a practical method for UQ based on specific modeling scenarios and loss functions. Furthermore, the research extends this view to learning theory, analyzing subjective risk analogues of excess risk, approximation error, and estimation error, and connecting them to UQ. AI
IMPACT This research provides a unified theoretical foundation for uncertainty quantification methods, potentially leading to more robust AI models.
RANK_REASON The cluster contains two identical arXiv preprints detailing a new theoretical framework for uncertainty quantification.
- aleatoric uncertainty
- approximation error
- epistemic uncertainty
- learning theory
- reverse cross-entropy
- strictly proper loss
- Subjective Risk Decomposition
- uncertainty quantification
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →
